The visual organization: data visualization, big data, and the quest for better decisions
Saved in:
| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Hoboken, NJ
Wiley
2014
|
| Series: | Wiley & SAS Business Series
|
| Subjects: | |
| Online Access: | Inhaltstext Inhaltsverzeichnis |
| Physical Description: | XXVIII, 202 S. Ill., graph. Darst. 250 mm x 150 mm |
| ISBN: | 9781118794388 |
Staff View
MARC
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Contents
List of Figures and Tables
xvii
Preface
xix
Acknowledgments
xxv
how to Help This Book
xxvii
Part
ι
Book overview and Background
1
Introduction
3
Adventures in Twitter Data Discovery
4
Contemporary Dataviz
101 9
Primary Objective
9
Benefits
11
More Important Than Ever
13
Revenge of the Laggards: The Current State of Dataviz
1 5
Book Overview
18
Defining the Visual Organization
19
Central Thesis of Book
19
Cui Bono?
20
Methodology: Story Matters Here
21
The Quest for Knowledge and Case Studies
24
Differentiation: A Note on Other Dataviz Texts
25
Plan of Attack
26
Next
27
Notes
27
Chapter
1
The Ascent of the Visual Organization
29
The Rise of Big Data
30
Open Data
30
The Burgeoning Data Ecosystem
33
The New Web: Visual, Semantic, and API-Driven
34
The Arrival of the Visual Web
34
Linked Data and a More Semantic Web
3 5
The Relative Ease of Accessing Data
36
Greater Efficiency via Clouds and Data Centers
37
Better Data Tools
38
Greater Organizational Transparency
40
The Copycat Economy: Monkey See, Monkey Do
41
Data Journalism and the Nate Silver Effect
41
Digital Man
44
The Arrival of the Visual Citizen
44
Mobility
47
xiii
► CONTENTS
The Visual Employee: A More Tech- and Data-Savvy Workforce
47
Navigating Our Data-Driven World
48
Next
49
Notes
49
Chapter
2
Transforming Data into Insights: The Tools
51
Dataviz: Part of an Intelligent and Holistic Strategy
52
The Tyranny of Terminology: Dataviz, BI, Reporting, Analytics, and
KPIs
5 3
Do Visual Organizations Eschew All Trie
d
-and-True Reporting
Tools?
5 5
Drawing Some Distinctions
56
The Dataviz Fab Five
57
Applications from Large Enterprise Software Vendors
57
LESVs: The Case For
58
LESVs: The Case Against
59
Best-of-Breed Applications
61
Cost
62
Ease of Use and Employee Training
62
Integration and the Big Data World
63
Popular Open-Source Tools
64
D3.JS
64
R
65
Others
66
Design Firms
66
Startups, Web Services, and Additional Resources
70
The Final Word: One Size Doesn't Fit All
72
Next
73
Notes
73
Part:
n
introducing the Visuai Organization
75
¡Ί
apter
3
The Quintessential Visual Organization
77
Netflix
1.0:
Upsetting the Applecart
77
Netflix
2.0:
Self-Cannibalization
78
Dataviz: Part of a Holistic Big Data Strategy
80
Dataviz: Imbued in the Netflix Culture
81
Customer Insights
82
Better Technical and Network Diagnostics
84
Embracing the Community
88
Lessons
89
Next
90
Notes
90
Chapter
4
Dataviz in the
DNA 93
The Beginnings
94
UX Is Paramount
95
The Plumbing
97
Embracing Free and Open-Source Tools
98
Extensive Use of APIs
101
Lessons
101
Next
102
Note
102
Chapter
5
Transparency in Texas
103
Background
104
Early Dataviz Efforts
10 5
Embracing Traditional BI
106
Data Discovery
107
Better Visibility into Student Life
108
Expansion: Spreading Dataviz Throughout the System
110
CONTENTS 4
XV
Results 111
Lessons
113
Next
113
Notes
114
Partili
Getting Started: Becoming a Visual Organization
115
Chapter
6
The Four-Level visual Organization Framework
117
Big Disclaimers
118
A Simple Model
119
Limits and Clarifications
120
Progression
122
Is Progression Always Linear?
123
Can a Small Organization Best Position Itself to Reach Levels
3
and
4?
If So, How?
123
Can an Organization Start at Level
3
or
4
and Build from the Top
Down?
123
Is Intralevel Progression Possible?
123
Are Intralevel and Interlevel Progression Inevitable?
123
Can Different Parts of the Organization Exist on Different
Levels?
124
Should an Organization Struggling with Levels
1
and
2
Attempt to
Move to Level
3
or
4? 124
Regression: Reversion to Lower Levels
124
Complements, Not Substitutes
125
Accumulated Advantage
125
The Limits of Lower Levels
125
Relativity and
Subleveis
125
Should Every Organization Aspire to Level
4? 126
Next
126
Chapter
7
WWVOD?
127
Visualizing the Impact of a Reorg
128
Visualizing Employee Movement
129
Starting Down the Dataviz Path
129
Results and Lessons
133
Future
135
A Marketing Example
136
Next
137
Notes
137
Chapter
8
Building the Visual Organization
139
Data Tips and Best Practices
139
Data: The Primordial Soup
139
Walk Before You Run
.
At Least for Now
140
A Dataviz Is Often Just the Starting Point
140
Visualize Both Small and Big Data
141
Don't Forget the Metadata
141
Look Outside of the Enterprise
143
The Beginnings: All Data Is Not Required
143
Visualize Good and Bad Data
144
Enable Drill-Down
144
Design Tips and Best Practices
148
Begin with the End in Mind (Sort of)
148
Subtract When Possible
150
UX: Participation and Experimentation Are Paramount
150
Encourage Interactivity
151
Use Motion and Animation Carefully
151
Use Relative
—
Not Absolute
—
Figures
151
Technology Tips and Best Practices
152
Where Possible, Consider Using APIs
152
xvi
►contents
Embrace New Tools
152
Know the Limitations of Data viz Tools
153
Be Open
153
Management Tips and Best Practices
154
Encourage Self-Service, Exploration, and Data Democracy
154
Exhibit a Healthy Skepticism
154
Trust the Process, Not the Result
155
Avoid the Perils of Silos and Specialization
156
If Possible, Visualize
156
Seek Hybrids When Hiring
157
Think Direction First, Precision Later
157
Next
158
Notes
158
Chapter
9
The Inhibitors: Mistakes, Myths, and Challenges
159
Mistakes
160
Falling into the Traditional
ROI
Trap
160
Always
—
and Blindly
—
Trusting a Dataviz
161
Ignoring the Audience
162
Developing in a Cathedral
162
Set It and Forget It
162
Bad Dataviz
163
TMI
163
Using Tiny Graphics
163
Myths
165
Data-visualizations Guarantee Certainty and Success
165
Data Visualization Is Easy
165
Data Visualizations Are Projects
166
There Is One "Right" Visualization
166
Excel Is Sufficient
167
Challenges
167
The Quarterly Visualization Mentality
167
Data Defiance
168
Unlearning History: Overcoming the Disappointments of Prior
Tools
168
Next
169
Notes
169
Part IV Conclusion and the Future of Dataviz
171
Coda: we're Just Getting Started
1
73
Four Critical Data-Centric Trends
175
Wearable Technology and the Quantified Self
175
Machine Learning and the Internet of Things I
76
Multidimensional Data
177
The Forthcoming Battle Over Data Portability and Ownership
179
Final Thoughts: Nothing Stops This Train
181
Notes
182
Afterword: My Life in Data
183
Appendix: Supplemental Dataviz Resources
187
Selected Bibliography
191
About the Author
193
Index
195 |
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| discipline | Allgemeines Informatik Wirtschaftswissenschaften |
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| isbn | 9781118794388 |
| language | English |
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| spelling | Simon, Phil Verfasser aut The visual organization data visualization, big data, and the quest for better decisions Phil Simon Hoboken, NJ Wiley 2014 XXVIII, 202 S. Ill., graph. Darst. 250 mm x 150 mm txt rdacontent n rdamedia nc rdacarrier Wiley & SAS Business Series Visuelle Kommunikation (DE-588)4131112-7 gnd rswk-swf Visuelle Kommunikation (DE-588)4131112-7 s DE-604 Erscheint auch als Online-Ausgabe 978-1-118-85834-9 Erscheint auch als Online-Ausgabe 978-1-118-85841-7 X:MVB text/html http://deposit.dnb.de/cgi-bin/dokserv?id=4549339&prov=M&dok_var=1&dok_ext=htm Inhaltstext Digitalisierung UB Bamberg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027282684&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Simon, Phil The visual organization data visualization, big data, and the quest for better decisions Visuelle Kommunikation (DE-588)4131112-7 gnd |
| subject_GND | (DE-588)4131112-7 |
| title | The visual organization data visualization, big data, and the quest for better decisions |
| title_auth | The visual organization data visualization, big data, and the quest for better decisions |
| title_exact_search | The visual organization data visualization, big data, and the quest for better decisions |
| title_full | The visual organization data visualization, big data, and the quest for better decisions Phil Simon |
| title_fullStr | The visual organization data visualization, big data, and the quest for better decisions Phil Simon |
| title_full_unstemmed | The visual organization data visualization, big data, and the quest for better decisions Phil Simon |
| title_short | The visual organization |
| title_sort | the visual organization data visualization big data and the quest for better decisions |
| title_sub | data visualization, big data, and the quest for better decisions |
| topic | Visuelle Kommunikation (DE-588)4131112-7 gnd |
| topic_facet | Visuelle Kommunikation |
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